Coating color database creating method, search method using the database, their system, program, and recording medium

ABSTRACT

A method for creating a database for paint colors having a desired texture includes storing spectral reflectance data and micro-brilliance data of paint colors after associating each spectral reflectance data and each micro-brilliance data with a paint color code; storing texture evaluation values of sample paint colors after associating the each texture evaluation value with the paint color code; calculating characteristic quantities of the paint colors expressing textures using the spectral reflectance data and the micro-brilliance data, and storing the characteristic quantities after associating the each characteristic quantity with the paint color code; training a neural network using the characteristic quantities and the texture evaluation values of the sample paint colors as training data; and inputting characteristic quantities of the paint colors other than the sample paint colors into the neural network after the training, and storing output data after associating each output data with the paint color code.

This application is a Divisional of application Ser. No. 12/663,364, thecontents of which are hereby incorporated by reference, which is theNational Stage of International Application No. PCT/JP2008/061254, filedJun. 19, 2008.

TECHNICAL FIELD

The present invention relates to a paint color database. Morespecifically, the present invention relates to a method of creating adatabase for searching for a paint color having a desired texture or apaint color that belongs to a desired color category, a search methodusing the database, and a system, a program, and a recording mediumtherefor.

BACKGROUND ART

With regard to industrial products such as vehicles, the color issignificant in terms of the marketability. Generally, during the processof developing and designing industrial products such as vehicles, colorsare designed based on the function and concept of the products.

When coloring is done by applying paint, colors obtained by applicationare called “paint colors”. Usually, when designing colors, a colordesigner of a product (hereinafter referred to as a “user”) tells apaint manufacturer the texture desired for a paint color, and asks thepaint manufacturer to develop the paint color accordingly. The “texture”of a paint color denotes the impression received by an observer whenlooking at the paint color, as well as the impression given by the paintcolor. The paint manufacturer's engineer designs a paint color in viewof the texture desired by the user. The user evaluates the paint colordesigned by the paint manufacturer, and may request that the paint colorbe modified such that the paint color matches the desired texture; thepaint manufacturer's engineer then redesigns the paint color accordingto the request. Such a cycle is repeated between the user and the paintmanufacturer's engineer to complete the paint color desired by the user.

During the first stage of the paint color design cycle described above,the paint manufacturer's engineer understands the texture that the userdesires for the paint color, then searches for a paint color thatmatches the texture desired by the user among numerous paint colors thathave been designed (hereinafter referred to as stock colors), and thenpresents the paint color obtained through the search to the user forevaluation. At this time, if the paint color presented to the user bythe paint manufacturer is greatly different from the texture desired bythe user, the design process of the paint color must be started overfrom the first stage of the above-described cycle, resulting in a largenumber of industrial steps required for designing a paint color.

Usually, when designing a paint color, the texture desired by the useris conveyed to the paint manufacturer's engineer by terms expressingtextures such as translucent appearance, deepness appearance,three-dimensional appearance, etc. (hereinafter these words are referredto as “impression terms”). However, because the impression that onereceives from these impression terms varies depending on the individual,the texture desired by the user is often not accurately conveyed to thepaint manufacturer's engineer.

There have been attempts to quantify the textures expressed by theimpression terms. For example, Patent Document 1 below discloses amethod of quantifying the textures of metallic paint colors, such asmetallic appearance, clearness appearance, etc., based on evaluationvalues obtained from colorimetric values of the paint colors using aspecific function.

-   Patent Document 1: Japanese Unexamined Patent Publication No.    2003-279413-   Patent Document 2: Japanese Unexamined Patent Publication No.    H11-211569-   Non-Patent Document 1: Touru HIRAYAMA, Shin YAMANAGA, Shinichi    GAMOU, “Visual Evaluation and Digital Image Analysis of    Micro-brilliance (II)”, Research on Coatings, Kansai Paint Co.,    Ltd., No. 138, July 2002-   Non-Patent Document 2: Eiji NOMURA, Touru HIRAYAMA “Visual    Evaluation and Digital Image Analysis of Micro-brilliance”, Research    on Coatings, Kansai Paint Co., Ltd., No. 132, April 1999

DISCLOSURE OF THE INVENTION Problem to be Solved by the Invention

However, there is a problem with the method described in PatentDocument 1. That is, although the method quantifies specific textures ofmetallic paint colors so that the quantified values can be used forcomparison, the textures subjected to quantification are limited tothose of metallic paint colors, such as metallic appearance, clearnessappearance, and the like.

Further, the method described in Patent Document 1 has another problem.That is, the method can only be used when the colorimetric values(explanatory variables) and the textures (response variables) used formultiple linear regression analysis have a linear relationship. When thecolorimetric values and the textures have a nonlinear relationshipand/or when each of the colorimetric values is correlated with eachother, the textures cannot be accurately quantified.

The present invention is achieved to solve the above problems. Theobject is to provide a method of creating a database for determining apaint color having a desired texture or a paint color that belongs to adesired color category, a search method using the database, and asystem, a program, and a recording medium therefor.

Means for Solving the Problem

In order to solve the foregoing problem, a first method for creating apaint color database according to the present invention comprises: afirst step of storing spectral reflectance data and micro-brilliancedata expressing particle feeling of a plurality of paint colors in arecording unit after associating each spectral reflectance data and eachmicro-brilliance data with a code for specifying each paint color; asecond step of storing texture evaluation values of sample paint colorsselected from the plurality of paint colors in the recording unit afterassociating the each texture evaluation value with the code; a thirdstep of calculating characteristic quantities of the paint colorsexpressing textures using the spectral reflectance data and themicro-brilliance data, and storing the characteristic quantities in therecording unit after associating the each characteristic quantity withthe code; a fourth step of carrying out a process for training a neuralnetwork having input units corresponding to the characteristicquantities and output units corresponding to the texture evaluationvalues, using the characteristic quantities and the texture evaluationvalues of the sample paint colors as training data; and a fifth step ofinputting characteristic quantities of the paint colors other than thesample paint colors into the neural network after the training process,and storing output data in the recording unit after associating the eachoutput data with the code.

In addition to the feature of the first method, the second method forcreating a paint color database according to the present invention isfurther arranged so that the first step comprises a step of obtainingimage data of each paint color using an imaging device and calculatingmicro-brilliance data from the image data,

the micro-brilliance data are HG, HB, HBL and SB,

the HG has a condition such that:

in cases where IPSL≧0.32: HG=500·IPSL−142.5

in cases where 0.32>IPSL≧0.15: HG=102.9·IPSL−15.4

in cases where 0.15>IPSL: HG=0

the HB has a condition such that:

HB=(BV−50)/2

the HBL is an HB found from the image data taken under a condition whereGL=14, and the SB is an HG found from the image data taken under acondition where GL=125,

wherein GL represents an average gray level of image data, V and Arespectively represent gross luminance volume and gross luminance areagreater than a threshold=“GL+32” binarized by the threshold, Lrepresents an average particle diameter binarized by athreshold=“GL+24”, PHav and PSav respectively represent an average peakheight and an average peak skirt of luminance image that satisfy:PHav=3V/A, PSav=L/PHav and BV=PHav+350PSav,

IPSL satisfies:

IPSL=∫₀ ^(N)∫₀ ^(2π) P(ν,θ)dνdθ/P(0,0)

wherein ν represents spatial frequency, θ represents angle, P(ν,θ)represents power spectrum found from the image data, and 0 to N arespatial frequency regions for the particle feeling.

In addition to the feature of the first method, the third method forcreating a paint color database according to the present invention isfurther arranged so that the characteristic quantities include: acharacteristic quantity L*₁₅, a characteristic quantity a*₁₅, and acharacteristic quantity b*₁₅, which are L* value, a* value, and b* valuecalculated from a spectral reflectance at an observation angle of 15° inthe L*a*b* color space, a characteristic quantity L^(*) ₄₅, acharacteristic quantity a*₄₅, and a characteristic quantity b*₄₅, whichare L* value, a* value, and b* value calculated from a spectralreflectance at an observation angle of 45° in the L*a*b* color space, acharacteristic quantity L^(*) ₇₅, a characteristic quantity a*₇₅, and acharacteristic quantity b*₇₅, which are L* value, a* value, and b* valuecalculated from a spectral reflectance at an observation angle of 75° inthe L*a*b* color space; a characteristic quantity FF(15,25), acharacteristic quantity FF(25,45), a characteristic quantity FF(45,75),a characteristic quantity FF(75,110) and a characteristic quantityFF(15,45), which are found according to 2×(Y₁₅−Y₂₅)/(Y₁₅+Y₂₅),2×(Y₂₅−Y₄₅)/(Y₂₅±Y₄₅), 2×(Y₄₅−Y₇₅)/(Y₄₅+Y₇₅), 2×(Y₇₅−Y₁₁₀)/(Y₇₅+Y₁₁₀)and 2×(Y₁₅−Y₄₅)/(Y₁₅+Y₄₅), respectively, wherein Y₁₅ represents a Yvalue calculated from a spectral reflectance at an observation angle of15° in the XYZ color space, Y₂₅ represents a Y value calculated from aspectral reflectance at an observation angle of 25° in the XYZ colorspace, Y₄₅ represents a Y value calculated from a spectral reflectanceat an observation angle of 45° in the XYZ color space, Y₇₅ represents aY value calculated from a spectral reflectance at an observation angleof 75° in the XYZ color space, Y₁₁₀ represents a Y value calculated froma spectral reflectance at an observation angle of 110° in the XYZ colorspace; a characteristic quantity c*₁₅ that is a c* value calculated froma spectral reflectance at an observation angle of 15° in the L*C*h*color space, a characteristic quantity c*₄₅ that is a c* valuecalculated from a spectral reflectance at an observation angle of 45° inthe L*C*h* color space, and a characteristic quantity c*₇₅ that is a c*value calculated from a spectral reflectance at an observation angle of75° in the L*C*h* color space; a characteristic quantity cFF(15,25) thatis found according to 2×(c*₁₅−c*₂₅)/(c*₁₅+c*₂₅), a characteristicquantity cFF(25,45) that is found according to 2×(c*₂₅−c*₄₅)/(c*₂₅+c*₄₅)a characteristic quantity cFF(45,75) that is found according to2×(c*₄₅−c*₇₅)/(c*₄₅+c*₇₅), a characteristic quantity cFF(75,110) that isfound according to 2×(c*₇₅−c*₁₁₀)/(c*₇₅+c*₁₁₀), and a characteristicquantity cFF(15,45) that is found according to 2×(c*₁₅−c₄₅)/(c*₁₅+C*₄₅)wherein c*₂₅ represents a c* value calculated from a spectralreflectance at an observation angle of 25° in the L*C*h* color space,and c*₁₁₀ represents a c* value calculated from a spectral reflectanceat an observation angle of 110° in the L*C*h* color space; andcharacteristic quantities HG, HB, HBL and SB which are themicro-brilliance data.

A fourth method for creating a paint color database according to thepresent invention comprises: a first step of storing spectralreflectance data of a plurality of paint colors in a recording unitafter associating each spectral reflectance data with a code forspecifying each paint color; a second step of storing color categoryevaluation values of sample paint colors selected from the plurality ofpaint colors in the recording unit after associating the each colorcategory evaluation value with the code; a third step of calculatingcharacteristic quantities of the paint colors expressing colorcategories using the spectral reflectance data, and storing thecharacteristic quantities in the recording unit after associating theeach characteristic quantity with the code; a fourth step of carryingout a process for training a neural network having input unitscorresponding to the characteristic quantities and output unitscorresponding to the color category evaluation values, using thecharacteristic quantities and the color category evaluation values ofthe sample paint colors as training data; and a fifth step of inputtingcharacteristic quantities of the paint colors other than the samplepaint colors into the neural network after the training process, andstoring output data in the recording unit after associating the eachoutput data with the code.

In addition to the feature of the fourth method, the fifth method forcreating a paint color database according to the present invention isfurther arranged so that the characteristic quantities include: acharacteristic quantity L*₁₅, a characteristic quantity a*₁₅, and acharacteristic quantity b*₁₅, which are L* value, a* value, and b* valuecalculated from a spectral reflectance at an observation angle of 15° inthe L*a*b* color space, a characteristic quantity L^(*) ₄₅, acharacteristic quantity a*₄₅, and a characteristic quantity b*₄₅, whichare L* value, a* value, and b* value calculated from a spectralreflectance at an observation angle of 45° in the L*a*b* color space, acharacteristic quantity L*₁₁₀, a characteristic quantity a*₁₁₀, and acharacteristic quantity b*₁₁₀, which are L* value, a* value, and b*value calculated from a spectral reflectance at an observation angle of110° in the L*a*b* color space; a characteristic quantity c*₁₅ that is ac* value calculated from a spectral reflectance at an observation angleof 15° in the L*C*h* color space, a characteristic quantity c*₄₅ that isa c* value calculated from a spectral reflectance at an observationangle of 45° in the L*C*h* color space, and a characteristic quantityc*₁₁₀ that is a c* value calculated from a spectral reflectance at anobservation angle of 110° in the L*C*h* color space; a characteristicquantity sin(h₁₅); a characteristic quantity cos(h₁₅), a characteristicquantity sin(h₄₅), a characteristic quantity cos(h₄₅), a characteristicquantity sin(h₁₁₀) and a characteristic quantity cos(h₁₁₀), wherein h₁₅represents a hue angle at an observation angle of 15°, h₄₅ represents ahue angle at an observation angle of 45°, h₁₁₀ represents a hue angle atan observation angle of 110°, which are found from the a*₁₅, b*₁₅, a*₄₅,b*₄₅, a*₁₁₀, and b*₁₁₀ according to h=tan⁻¹(b*/a*); and a characteristicquantity FF(15,45), which is found according to 2×(Y₁₅−Y₄₅)/(Y₁₅+Y₄₅)wherein Y₁₅ represents a Y value calculated from a spectral reflectanceat an observation angle of 15° in the XYZ color space, and Y₄₅represents a Y value calculated from a spectral reflectance at anobservation angle of 45° in the XYZ color space.

A first method for searching for a paint color according to the presentinvention is a method for searching for a paint color in a paint colordatabase created by the first method for creating a paint colordatabase, comprising: a first step of receiving, as a search query,texture and an evaluation value that denotes a degree of presence of thetexture; a second step of retrieving an evaluation value correspondingto the texture from the database, and determining whether the evaluationvalue is a value indicating the presence of the texture; and a thirdstep of, if determining that the evaluation value is a value indicatingthe presence of the texture in the second step, giving a correspondingpaint color code as a search result.

A second method for searching for a paint color according to the presentinvention is a method for searching for a paint color in a paint colordatabase created by the fourth method for creating a paint colordatabase, comprising: a first step of receiving, as a search query, acolor category and an evaluation value that denotes a degree ofattribution to the color category; a second step of retrieving anevaluation value corresponding to the color category from the database,and determining whether the evaluation value is a value indicating theattribution to the color category; and a third step of, if determiningthat the evaluation value is a value indicating the attribution to thecolor category in the second step, giving a corresponding paint colorcode as a search result.

A first system for creating a paint color database comprises: anarithmetic unit having a recording unit; a spectrophotometer; and animaging device, wherein: the arithmetic unit measures spectralreflectance data for each of a plurality of paint colors using thespectrophotometer, the arithmetic unit obtains image data of the paintcolors using the imaging device, and calculates micro-brilliance dataexpressing particle feeling of the paint colors from the image data, thearithmetic unit stores the spectral reflectance data and themicro-brilliance data of the paint colors in the recording unit afterassociating each spectral reflectance data and each micro-brilliancedata with a code for specifying each paint color, the arithmetic unitstores texture evaluation values of sample paint colors selected fromthe paint colors in the recording unit after associating the eachtexture evaluation value with the code, the arithmetic unit calculatescharacteristic quantities of the paint colors expressing textures usingthe spectral reflectance data and the micro-brilliance data, and storesthe characteristic quantities in the recording unit after associatingthe each characteristic quantity with the code, the arithmetic unitcarries out a process for training a neural network having input unitscorresponding to the characteristic quantities and output unitscorresponding to the texture evaluation values, using the characteristicquantities and the texture evaluation values of the sample paint colorsas training data; and the arithmetic unit inputs characteristicquantities of the paint colors other than the sample paint colors intothe neural network after the training process, and stores output data inthe recording unit after associating the each output data with the code.

A second system for creating a paint color database comprises: anarithmetic unit having a recording unit; and a spectrophotometer,wherein: the arithmetic unit measures spectral reflectance data for eachof a plurality of paint colors using the spectrophotometer, thearithmetic unit stores the spectral reflectance data of the paint colorsin the recording unit after associating each spectral reflectance datawith a code for specifying each paint color, the arithmetic unit storescolor category evaluation values of sample paint colors selected fromthe paint colors in the recording unit after associating the each colorcategory evaluation value with the code, the arithmetic unit calculatescharacteristic quantities of the paint colors expressing colorcategories using the spectral reflectance data, and stores thecharacteristic quantities in the recording unit after associating theeach characteristic quantity with the code, the arithmetic unit carriesout a process for training a neural network having input unitscorresponding to the characteristic quantities and output unitscorresponding to the color category evaluation values, using thecharacteristic quantities and the color category evaluation values ofthe sample paint colors as training data; and the arithmetic unit inputscharacteristic quantities of the paint colors other than the samplepaint colors into the neural network after the training process, andstores output data in the recording unit after associating the eachoutput data with the code.

A first system for searching for a paint color according to the presentinvention is a system for searching for a paint color in a paint colordatabase created by the first method for creating a paint colordatabase, comprising: an arithmetic unit having a recording unit storingthe database, wherein: the arithmetic unit receives, as a search query,texture and an evaluation value that denotes a degree of presence of thetexture, the arithmetic unit retrieves an evaluation value correspondingto the texture from the database, and determines whether the evaluationvalue is a value indicating the presence of the texture; and ifdetermining that the evaluation value is a value indicating the presenceof the texture in the second step, the arithmetic unit gives acorresponding paint color code as a search result.

A second system for searching for a paint color according to the presentinvention is a system for searching for a paint color in a paint colordatabase created by the fourth method for creating a paint colordatabase, comprising: an arithmetic unit having a recording unit storingthe database, wherein: the arithmetic unit receives, as a search query,a color category and an evaluation value that denotes a degree ofattribution to the color category, the arithmetic unit retrieves anevaluation value corresponding to the color category from the database,and determines whether the evaluation value is a value indicating theattribution to the color category; and if determining that theevaluation value is a value indicating the attribution to the colorcategory in the second step, the arithmetic unit gives a correspondingpaint color code as a search result.

A first program for creating a paint color database causes a computer torealize: a first function for storing spectral reflectance data andmicro-brilliance data expressing particle feeling of a plurality ofpaint colors in a recording unit after associating each spectralreflectance data and each micro-brilliance data with a code forspecifying each paint color; a second function for storing textureevaluation values of sample paint colors selected from the plurality ofpaint colors in the recording unit after associating the each textureevaluation value with the code; a third function for calculatingcharacteristic quantities of the paint colors expressing textures usingthe spectral reflectance data and the micro-brilliance data, and storingthe characteristic quantities in the recording unit after associatingthe each characteristic quantity with the code; a fourth function forcarrying out a process for training a neural network having input unitscorresponding to the characteristic quantities and output unitscorresponding to the texture evaluation values, using the characteristicquantities and the texture evaluation values of the sample paint colorsas training data; and a fifth function for inputting characteristicquantities of the paint colors other than the sample paint colors intothe neural network after the training process, and storing output datain the recording unit after associating the each output data with thecode.

A second program for creating a paint color database causes a computerto realize: a first function for storing spectral reflectance data of aplurality of paint colors in a recording unit after associating eachspectral reflectance data with a code for specifying each paint color; asecond function for storing color category evaluation values of samplepaint colors selected from the plurality of paint colors in therecording unit after associating the each color category evaluationvalue with the code; a third function for calculating characteristicquantities of the paint colors expressing color categories using thespectral reflectance data, and storing the characteristic quantities inthe recording unit after associating the each characteristic quantitywith the code; a fourth function for carrying out a process for traininga neural network having input units corresponding to the characteristicquantities and output units corresponding to the color categoryevaluation values, using the characteristic quantities and the colorcategory evaluation values of the sample paint colors as training data;and a fifth function for inputting characteristic quantities of thepaint colors other than the sample paint colors into the neural networkafter the training process, and storing output data in the recordingunit after associating the each output data with the code.

A first program for searching for a paint color, according to thepresent invention is a program for searching for a paint color in apaint color database created by the first method for creating a paintcolor database by causing a computer to realize: a first function ofreceiving, as a search query, texture and an evaluation value thatdenotes a degree of presence of the texture; a second function ofretrieving an evaluation value corresponding to the texture from thedatabase, and determining whether the evaluation value is a valueindicating the presence of the texture; and a third function of, ifdetermining that the evaluation value is a value indicating the presenceof the texture in the second step, giving a corresponding paint colorcode as a search result.

A second program for searching for a paint color according to thepresent invention is a program for searching for a paint color in apaint color database created by the fourth method for creating a paintcolor database by causing a computer to realize: a first function ofreceiving, as a search query, a color category and an evaluation valuethat denotes a degree of attribution to the color category; a secondfunction of retrieving an evaluation value corresponding to the colorcategory from the database, and determining whether the evaluation valueis a value indicating the attribution to the color category; and a thirdfunction of, if determining that the evaluation value is a valueindicating the attribution to the color category in the second step,giving a corresponding paint color code as a search result.

A computer-readable recording medium according to the present inventionstores the first program for creating a paint color database, the secondprogram for creating a paint color database, the first program forsearching for a paint color, or the second program for searching for apaint color.

Effects of the Invention

According to the present invention, textures of paint colors or colorcategories to which the paint colors belong are quantified usingcharacteristic quantities obtained from specific colorimetric values,thereby enabling the creation of a database in which the textures or thecolor categories of the paint colors are associated with codes specificto each paint color (hereinafter referred to as “paint color codes”).

Further, a paint color that matches a desired texture or a paint colorthat belongs to a desired color category can be searched for in thedatabase by specifying, as the search query, the color category and/orthe texture conveyed from the user when designing the paint color.

As for a metallic paint color whose appearance varies depending on theobservation angle, it is critical which data observed at what angleshould be used to categorize the color for matching an individual'scolor recognition. In this regard, according to the present invention, ametallic paint color that matches a desired texture can be searched forin a specific color category by specifying, as the search query, boththe color category and the texture conveyed from the user when designingthe paint color.

Further, a set of stock colors can be narrowed down with high accuracyto a paint color that matches a desired texture, or a paint color thatbelongs to a desired color category, thus reducing the number ofindustrial steps required for designing the paint color.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 A block diagram showing a schematic structure of a system forcreating and searching a texture database according to a firstembodiment of the present invention.

FIG. 2 A flow chart showing a method of creating the texture databaseaccording to the first embodiment of the present invention.

FIG. 3 A drawing showing an example of a structure of a neural networkused for creating the texture database according to the first embodimentof the present invention.

FIG. 4 A flow chart showing a method of searching the texture databasewhich the system shown in FIG. 1 performs.

FIG. 5 A drawing showing an example of an input screen of the system.

FIG. 6 A drawing showing an example of an output screen of the system.

FIG. 7 A flow chart showing a method of creating a color categorydatabase according to a second embodiment of the present invention.

REFERENCE NUMERALS

-   1 arithmetic unit-   2 spectrophotometer-   3 display apparatus-   4 imaging device-   5 coating film-   11 CPU-   12 memory-   13 recording unit-   14 bus-   15 operating unit-   16 interface unit-   51 entry field    -   52 paint color code    -   53 computer graphic image

BEST MODE FOR CARRYING OUT THE INVENTION

One embodiment of the present invention is described below in detailwith reference to the attached figures.

In the present specification, “highlight” denotes the case of observinga paint color at an angle variation (an angle within the plane verticalto the surface of the coating film) from the specular reflectiondirection, i.e., at an angle of 10° to 25° from the specular reflectiondirection when the film coated with the paint color (coating film,hereinafter) is irradiated with light at 45° (an angle within the planevertical to the surface of the coating film) with respect to itssurface; “shade” denotes the case of observing a paint color at an angleof 75° to 110° from the specular reflection direction; and “face”denotes the case of observing a paint color at an angle between thehighlight angle and the shade angle. Further, the micro-brillianceappearance denotes a texture expressed by the luster pigment in thecoating film, which is perceivable in the microscopic observation.

The first embodiment of the present invention creates a databaseregarding textures (texture database, hereinafter) of paint colors, andcarries out a search for a paint color that matches a desired texturefrom the created texture database.

FIG. 1 is a block diagram showing a schematic structure of a system forcreating a texture database with which the search for a desired color iscarried out. The system comprises an arithmetic unit 1, aspectrophotometer 2, a display apparatus 3 and an imaging device 4.

The arithmetic unit 1 includes: a CPU 11 configured to control thecomponential units of the system and execute the data processingdescribed below; a memory 12; a recording unit 13 for storing thetexture database described below; a bus 14 for carrying out datatransmission between the componential units; an operating unit 15 forreceiving external operations; and an interface unit (hereinafterreferred to as an I/F unit) 16 for performing data input/output betweenthe operating unit 15 and an external apparatus. The arithmetic unit 1causes the spectrophotometer 2 to measure a spectral reflectance of thecoating film 5, causes the imaging device 4 to take an image of thecoating film 5, and obtains the spectral reflectance data and the imagedata via the I/F unit 16. The image display apparatus 3 is, for example,a display capable of full-color display. Via the I/F unit 16, thearithmetic unit 1 causes the image display apparatus 3 to display asearch result of a desired paint color in a predetermined format, ordisplay various kinds of information items obtained at each stage of theprocess. The imaging device 4 is, for example, a CCD camera.

The following schematically describes the first embodiment of thepresent invention. First, a plurality of spectral reflectance of thecoating film 5 are measured for each stock color using thespectrophotometer 2, and image data of the coating film 5 is obtainedusing the imaging device 4 and various kinds of information items, suchas spectral reflectances measured at plural observation angles,micro-brilliance appearance, formulation, material of the coatingformulation, applying method of a test panel, computer graphic image,coating film performance, cost and the like (paint color-relatedinformation, hereinafter) is associated with each paint color code. Theassociated data sets are previously stored in the recording unit 13.Next, for each sample paint color, an evaluation value of the texturerepresented by an impression term, which is determined by an experienceddesigner, is associated with each paint color code. The associated dataare stored in the recording unit 13. Then, characteristic quantities arecalculated for the each stock color based on the spectral reflectancedata and the micro-brilliance appearance data. Thereafter, a neuralnetwork is constructed using the characteristic quantities of the samplepaint colors as an input unit and using the impression terms of thesample paint colors as an output unit; and the network is subjected tothe training process, thereby determining weights of connection(synaptic weight data). Using the determined neural network, the textureevaluation values for the stock colors are found, and the obtainedtexture evaluation values are associated with the paint color codes whenstored in the recording unit 13 as a texture database. When the valuefor specifying the presence of the texture expressed by an impressionterm is inputted, as a search query, from the operating unit 15, asearch is carried out to find a paint color in the texture database thatmatches the search query; and the search results, i.e., a correspondingpaint color code and paint color-related information, are displayed inthe display apparatus 3.

The following first explains a method of creating a texture database,and, secondly, a paint color search method using the created texturedatabase. FIG. 2 is a flow chart showing a method of creating a texturedatabase according to the first embodiment of the present invention.

The following steps are carried out by the arithmetic unit 1, unlessotherwise specified. The process carried out by the arithmetic unit 1 isa process carried out by the CPU 11. The CPU 11 temporarily stores allof the necessary data items (setting values, data being processed etc.)in the memory 12, which serves as a working space. The CPU 11 alsostores data items in the recording unit 13 in the following processingmanner. The CPU 11 further displays the data stored in the memory 12 orthe recording unit 13 as an image or text on the display apparatus 3.The paint color-related information for each stock color is associatedwith a corresponding paint color code and stored in the recording unit13 in advance. Color sample cards of these stock colors are used as thecoating films 5.

In step S11, the spectral reflectance data and the micro-brillianceappearance data for each stock color is associated with a correspondingpaint color code, and is stored in the recording unit. First, thesurface of the coating film 5 is irradiated with light at an irradiationangle of 45° (an angle within the plane vertical to the surface of thecoating film). Then, spectral reflectances are measured using aspectrophotometer 2 at five observation angles (may also be referred toas light-receiving angles) 15°, 25°, 45°, 75°, and 110° as five anglevariations (angles within the plane vertical to the surface of thecoating film) of the specular reflection light direction. Thespectrophotometer 2 is realized by, for example, a multi-anglespectrophotometer MA68II (X-Rite, Inc.). The measured spectralreflectance data is transmitted to the arithmetic unit 1 via the I/Funit 16, and is associated with information indicating the anglevariation at the measurement and a corresponding paint color code. Theassociated data is stored in the recording unit 13.

Next, the image data of the coating film 5 is obtained using an imagingdevice 4. Based on the obtained image data, the micro-brillianceappearances expressed as HG, HB, HBL, and SB are calculated. Thecalculated micro-brilliance appearance data (HG, HB, HBL, and SB) areassociated with the paint color code, and the associated data is storedin the recording unit 13. Since HG, HB, HBL, and SB are publicly known,and their definition, measurement method and calculation method aredisclosed in Non-Patent Document 1, Non-Patent Document 2 etc., thefollowing explains them only briefly, and detailed descriptions areomitted.

Among the data items indicating the micro-brilliance appearances, HG(High-light Graininess) expresses particle feeling, and HB (High-lightBrilliance) expresses glittering appearance. HG and HB are foundaccording to the following formulas.

In cases where IPSL≧0.32: HG=500·IPSL−142.5

In cases where 0.32>IPSL≧0.15: HG=102.9·IPSL−15.4

In cases where 0.15>IPSL: HG=0

HB=(BV−50)/2

Here, IPSL (Integration of Power Spectrum of Low Frequency) and BV(Brilliance Value) are found according to the following formulas.

IPSL=∫₀ ^(N)∫₀ ^(2π) P(ν,θ)dνdθ/P(0,0)

BV=PHav+350PSav

In these formulas, P(ν,θ) represents a power spectrum obtained bysubjecting two-dimensional luminance distribution data generated by theobtained image data to a two-dimensional Fourier transform, ν representsa spatial frequency, and θ represents an angle. 0 to N are spatialfrequency regions for the particle feeling. PHav and PSav respectivelyrepresent an average peak height and an average peak skirt of theluminance image, wherein PHav=3V/A and PSav=L/PHay. Here, V and Arespectively represent the gross luminance volume and the grossluminance area greater than a threshold=“an average gray level of imagedata (GL, hereinafter)+32” binarized by the threshold. L represents anaverage particle diameter binarized by a threshold=“GL+24”.

Among the data items indicating the micro-brilliance appearances, HBL(High-light Brilliance at Low-illuminance) is found from image datataken under low light exposure conditions, i.e., GL=14, using the aboveformula for finding HB. SB (Shade Brilliance) is an HG taken under acondition where GL=125.

In Step S12, sample paint colors are selected from the set of stockedpaint colors to be used for the later-described neural network training.In the first embodiment of the present invention, the textures of thepaint colors are expressed by, for example, the 10 kinds of impressionterms shown in Table 1.

TABLE 1 Impression Term Definition Deepness Highly chromatic, butbrightness is not high in the high- Appearance light side; not cloudy,and is a black texture in the shade side (lowered when contrast betweenthe highlight side and the shade side is too strong). Three-Laminate-like particulate texture in the area from the dimensionalhighlight side to the shade side. Appearance Chromaticness Chromatic andvivid texture. Translucent Highly brightened texture, and shines whiteat the Appearance highlight side. Nuance Texture with different colorphases between the highlight side and shade side (lowered when the colorphases are clearly distinguishable). Solid Texture with little colorchange in the shade side (textures with no color change are excluded).Color Band Color recognition texture towards the direction of the shadeside. Smooth Dense texture with a moderate change in brightness, andAppearance having a film coating-like appearance. Metallic Highlybrightened, not particulate but dense texture in the Appearancehighlight side. Difference in brightness between the highlight side andthe shade side. Clear Feeling No metallic texture with lowchromaticness. Difference in brightness between the highlight side andthe shade side.

It is preferable to select sample paint colors having textures expressedby the impression terms shown in Table 1, while avoiding bias toward aspecific texture.

In Step S13, the textures of the selected sample paint colors areevaluated, the resulting evaluation values are associated with paintcolor codes, and the associated data are stored in the recording unit.First, the computer graphic image of each sample paint color stored inthe recording unit 13 is displayed in the display apparatus 3, and aplurality of experienced designers evaluate the texture of the samplepaint color for each of the textures expressed by the impression termsshown in Table 1. The evaluation results are thereafter inputted via theoperating unit 15. By repeating this process, the texture evaluationsheet shown in Table 2 is created, which is in the form of electronicdata or the like. The texture evaluation sheet shows a plurality oftexture evaluation values expressed by the impression terms (10 kinds)for each sample paint color. As the texture evaluation value, “1” is setwhen the color has the texture, and “0” is set when the color does nothave the texture. For example, for the sample paint color 01 in Table 2,the sample paint color was evaluated to have only solidity and colorband; therefore the value “1” is set in the corresponding cell, whilethe value “0” is set in the remaining cells. The texture evaluationvalues recorded in the texture evaluation sheet are associated with thepaint color codes, and are stored in the recording unit 13.

TABLE 2 Three- Deepness dimensional Translucent Color Smooth ClearMetallic Appearance Appearance Chromaticness Appearance Nuance SolidBand Appearance Feeling Appearance Sample 01 0 0 0 0 0 1 1 0 0 0 Sample02 0 0 1 0 1 0 0 0 1 1 Sample 03 0 0 0 1 0 0 0 0 0 1 Sample 04 0 0 0 0 00 0 0 0 1 Sample 05 0 1 0 0 1 0 0 0 0 1 Sample 06 0 1 0 0 0 0 0 0 0 1Sample 07 0 0 0 1 0 0 0 0 0 1 Sample 08 0 0 0 0 0 0 0 0 1 1 Sample 09 11 0 0 1 0 0 0 0 0 Sample 10 0 1 0 0 0 0 0 0 1 1 Sample 11 0 1 0 0 1 0 01 0 0 Sample 12 0 1 0 0 1 0 0 0 0 1 Sample 13 0 0 0 0 0 0 1 0 0 0 — — —— — — — — — — — Sample 200 0 0 0 0 1 1 0 0 0 0

In Step S14, using the spectral reflectance data, characteristicquantities (26 kinds) expressing the following textures are calculatedfor each stock color. The calculation results are associated with thepaint color codes and stored in the recording unit 13.

First, based on the spectral reflectance at 15°, a characteristicquantity L*₁₅, a characteristic quantity a*₁₅, and a characteristicquantity b*₁₅, which are L* value (brightness), a* value, and b* value(respectively L*₁₅, a*₁₅, and b*₁₅, hereinafter) at the observationangle of 15° in the L*a*b* color space, are calculated. Similarly, basedon the spectral reflectance at 45°, a characteristic quantity L*₄₅, acharacteristic quantity a*₄₅, and a characteristic quantity b*₄₅ arecalculated. Further, based on the spectral reflectance at 75°, acharacteristic quantity L*₇₅, a characteristic quantity a*₇₅, and acharacteristic quantity b*₇₅ are calculated.

Next, based on the spectral reflectance at 15°, a Y value (brightness)(Y₁₅, hereinafter) in the XYZ color space is calculated. Similarly, Y₂₅,Y₄₅, Y₇₅ and Y₁₁₀ are calculated based on the spectral reflectances at25°, 45°, 75° and 110°, respectively. Next, a characteristic quantityFF(15,25) is calculated according to 2×(Y₁₅−Y₂₅)/(Y₁₅+Y₂₅). Similarly, acharacteristic quantity FF(25,45), a characteristic quantity FF(45,75),a characteristic quantity FF(75,110) and a characteristic quantityFF(15,45) are calculated according to 2×(Y₂₅−Y₄₅)/(Y₂₅+Y₄₅),2×(Y₄₅−Y₇₅)/(Y₄₅+Y₇₅), 2×(Y₇₅−Y₁₁₀)/(Y₇₅+Y₁₁₀), and2×(Y₁₅−Y₄₅)/(Y₁₅+Y₄₅), respectively.

Next, based on the spectral reflectance at 15°, a characteristicquantity c*₁₅, which is a c* value (chromaticness) (c*₁₅, hereinafter)in the L*C*h* color space, is calculated. Similarly, a characteristicquantity c*₄₅ and a characteristic quantity c*₇₅ are calculated based onthe spectral reflectances at 45° and at 75°, respectively. Similarly, acharacteristic quantity c*₂₅ and a characteristic quantity c*₁₁₀ arecalculated based on the spectral reflectances at 25° and at 110°,respectively. Next, a characteristic quantity cFF(15,25) is calculatedaccording to 2×(c*₁₅−c*₂₅)/(c*₁₅+c*₂₅). Similarly, a characteristicquantity cFF(25,45), a characteristic quantity cFF(45, 75), acharacteristic quantity cFF(75,110) and a characteristic quantitycFF(15,45) are calculated according to2×(c*₂₅−c*₄₅)/(c*₂₅+c*₄₅)×(c*₄₅−c*₇₅)/(c*₄₅+c*₇₅),2×(c*₇₅−c*₁₁₀)/(c*₇₅+c*₁₁₀) and 2×(c*₁₅−c*₄₅)/(c*₁₅+c*₄₅) respectively.

Further, the values of HG, HB, HBL and SB associated with the paintcolor codes in the recording unit 13 are also used as characteristicquantities. These 26 kinds of characteristic quantities thus obtainedare associated with the paint color code, and are stored in therecording unit 13.

In Step S15, a neural network is constructed, and a process for trainingthe neural network is carried out using the characteristic quantities ofthe sample paint colors as training data. FIG. 3 is a drawingillustrating a structure example of a neural network. The firstembodiment uses, as the algorithm of the neural network, an error backpropagation algorithm, which is one of the supervised training methods.Since error back propagation algorithms are publicly known, theexplanation thereof is omitted. As shown in FIG. 3, a neural networkhaving one intermediate layer consisting of 30 units is constructed. Inthe neural network, each unit of the input layer corresponds to one ofthe characteristic quantities (26 kinds) expressing the textures, whichare defined in Step S14, while each unit of the output layer correspondsto one of the impression terms (10 kinds). Then, after setting atraining rate (a real value ranging from 0 to 1), which is a parametershowing the training speed, and a permissible error (a real valueranging from 0 to 1), which is a permissive error of the output value inthe training, the process for training the neural network is carriedout. As a result, the characteristic quantities (26 kinds) expressingthe textures, found from the spectral reflectance measurement values,are associated with the texture evaluation values determined by theexperienced designers. The information for reconstructing the determinedneural network, i.e., information regarding the characteristicquantities and impression terms corresponding to the units of the inputlayer and the output layer, and the construction information of theneural network, such as synaptic weight data and the like of the neuralnetwork determined through the training, are stored in the recordingunit 13.

In Step S16, using the neural network after the training process, thetexture evaluation value is found for each stock color, and the obtainedtexture evaluation values are associated with each paint color code andstored in the recording unit 13, thereby completing a texture database.More specifically, a neural network is constructed using information forconstructing the neural network determined in Step S15, thecharacteristic quantities (26 kinds) for each stock color found in StepS14 are inputted to each unit of the input layer, and the valuesobtained from the units of the output layers (a real value ranging from0 to 1) are associated with the paint color code as correspondingtexture evaluation values and stored in the recording unit 13, therebycompleting a texture database.

The following describes a method of searching for a paint color having adesired texture from the created texture database. FIG. 4 is a flowchart showing a texture database search method performed by the systemshown in FIG. 1. FIG. 5 is a drawing illustrating one example of theinput screen of the system. FIG. 6 is a drawing illustrating one exampleof the output screen of the system.

In Step S21, a request for a desired texture is received as a searchquery. As shown in FIG. 5, when a value “1” (i.e., the color has thetexture) is set in the entry field 51 corresponding to the texture viathe operating unit 15, the texture is set as the search query. To setthe absence of the texture as the search query, the user who carries outthe search sets a value “0” (i.e., the color does not have the texture)in the entry field 51 corresponding to the texture. The texture with ablank entry field 51 is not used as the search query. For example, asshown in the input example of FIG. 5, when searching for a paint colorhaving a nuanced appearance and solidity, assuming that the parametercorresponding to the texture “nuanced appearance” is “nuance”, and theparameter corresponding to the texture “solidity” is “solid”, the searchquery is “nuance=1 and solid=1”.

In Step S22, a search is carried out in the texture database to find apaint color that matches the search query. The texture evaluation valuesfound by the neural network in Step S16, which is stored in therecording unit 13 as a database, are real values ranging from 0 to 1.When the texture evaluation value is a number other than 0 or 1, thedetermination as to whether the corresponding paint color has thedesired texture is carried out as follows. For example, a predeterminedthreshold (a real value ranging from 0 to 1) is set, and the paint coloris determined “to have the texture (corresponding parameter=1)” when thetexture evaluation value stored in the texture database is equal to orgreater than the threshold value, and the paint color is determined “notto have the texture (corresponding parameter=0)” when the textureevaluation value stored in the texture database is less than thethreshold value. For example, the threshold can be set to 0.5.

More specifically, in Step S22, among the evaluation values of thetextures (10 kinds) stored in the texture database, the relativemagnitude between the texture evaluation value (real value) specified bya search query “having the texture (corresponding parameter=1)” and thepredetermined threshold value is compared, thereby determining that thesaid paint color matches the search query or not. Among plural paintcolor codes of stock colors, the paint color code of the paint colorthat matches the query is given as a search result. For example, asshown in the input example of FIG. 5, when searching for a paint colorhaving a nuanced appearance and solidity, because the search query is“nuance=1 and solid=1”, a paint color code of a paint color having atexture evaluation value equal to or greater than the threshold 0.5 for“nuanced appearance” and having a texture evaluation value equal to orgreater than the threshold 0.5 for “solidity” is found among the textureevaluation values stored in the texture database, and the paint colorcode is given as a search result.

The following describes a method of searching for a paint color byspecifying the absence of a certain texture. For example, when searchingfor a paint color having solidity but not having a chromaticnessappearance, the user may input “1” in the “solid” column and “0” in the“chromaticness” column. As a result, assuming that the parametercorresponding to the texture “chromaticness” is “chroma”, the searchquery is “solid=1 and chroma=0”. When the threshold is 0.5, a paintcolor code of a paint color having a texture evaluation value equal toor greater than the threshold 0.5 for “solid” and having a textureevaluation value less than the threshold 0.5 for “chromaticness” isfound among the texture evaluation values stored in the texturedatabase, and the paint color code is given as a search result.

In Step S23, the paint color code and paint color-related information asthe search result are displayed in the display apparatus. As shown inFIG. 6, display apparatus 3 displays information items of the paintcolor that matches the search query i.e., the paint color-relatedinformation stored in the texture database, such as a paint color code52, a computer graphic image 53 of the paint color, and the like. Thepaint color as the search result can be displayed in the sorted form inorder of the evaluation values stored in the texture database, withrespect to the texture specified by the search query.

Next, the second embodiment of the present invention is described below.The second embodiment of the present invention creates a databaseregarding a color category of the paint colors (color category database,hereinafter), and carries out a search for a paint color that matches adesired color category from the created color category database. Thecolor category database according to the second embodiment of thepresent invention is created in a system having the same structure as inthe first embodiment shown in FIG. 1; therefore, the explanation of thesystem is omitted.

FIG. 7 is a flow chart showing a method of creating a color categorydatabase according to the second embodiment of the present invention.

In Step S31, the spectral reflectance data for each stock color isassociated with a corresponding paint color code, and is stored in therecording unit. As in Step S11, spectral reflectances are measured usingthe spectrophotometer 2 at three observation angles, 15°, 45°, and 110°.The measured spectral reflectance data are associated with acorresponding paint color code. The associated data is stored in therecording unit 13.

In Step S32, sample paint colors are selected from the set of stockcolors to be used for the later-described neural network training. Inthe second embodiment of the present invention, the paint colors areclassified into a plurality of categories. For example, each paint coloris given an attribute expressed by 10 kinds of terms (color categoryterms, hereinafter), i.e., white, silver, black, red, beige, yellow,green, turquoise, blue and purple. These color category terms correspondto the impression terms in the first embodiment. It is preferable toselect sample paint colors that appear to belong to the color categoriesexpressed by the color category terms, while avoiding bias toward aspecific color category.

In Step S33, the color categories of the selected sample paint colorsare evaluated, the resulting evaluation values are associated with paintcolor codes, and the associated data are stored in the recording unit.As in Step S13, a plurality of experienced designers evaluate the colorcategory of the sample paint color for each of the color categoriesexpressed by the color category terms. The evaluation results arethereafter inputted via the operating unit 15. By repeating thisprocess, a color category evaluation sheet shown in Table 3, which is inthe form of electronic data or the like, is created. The color categoryevaluation sheet shows a plurality of evaluation values of the colorcategories expressed by the color category terms (10 kinds) for eachsample paint color. As the evaluation value of the color category, “1”is set when the color appears to belong to the color category, and “0”is set when the color does not appear to belong to the color category.The color category evaluation values recorded in the color categoryevaluation sheet are associated with the paint color codes, and arestored in the recording unit 13. In the second embodiment, only onecolor category is determined for each sample paint color.

TABLE 3 White Silver Black Red Beige Yellow Green Turquoise Blue PurpleSample 01 0 0 0 0 0 1 0 0 0 0 Sample 02 0 0 1 0 0 0 0 0 0 0 Sample 03 10 0 0 0 0 0 0 0 0 Sample 04 0 0 0 0 0 0 0 0 0 1 Sample 05 0 1 0 0 0 0 00 0 0 Sample 06 0 1 0 0 0 0 0 0 0 0 Sample 07 0 0 0 1 0 0 0 0 0 0 Sample08 0 0 0 0 0 0 0 0 1 0 Sample 09 1 0 0 0 0 0 0 0 0 0 Sample 10 0 0 0 0 00 0 0 1 0 Sample 11 0 0 0 0 0 0 0 1 0 0 Sample 12 0 1 0 0 0 0 0 0 0 0Sample 13 0 0 0 0 0 0 1 0 0 0 — — — — — — — — — — — Sample 756 0 0 0 0 10 0 0 0 0

In Step S34, using the spectral reflectance data, characteristicquantities (19 kinds) expressing the following color categories arecalculated for each stock color. The calculation results are associatedwith the paint color codes and stored in the recording unit 13.

As in Step S14, first, based on the spectral reflectance at 15°, acharacteristic quantity L*₁₅, a characteristic quantity a*₁₅, and acharacteristic quantity b*₁₅, which are L* value, a* value, and b* valueat the observation angle of 15° in the L*a*b* color space, arecalculated. Similarly, based on the spectral reflectance at 45°, acharacteristic quantity L^(*) ₄₅ a characteristic quantity a*₄₅, and acharacteristic quantity b*₄₅ are calculated. Further, based on thespectral reflectance at 110°, a characteristic quantity L*₁₁₀, acharacteristic quantity a*₁₁₀, and a characteristic quantity b*₁₁₀ arecalculated.

Next, based on the spectral reflectance at 15°, a characteristicquantity c*₁₅, which is a c* value in the L*C*H* color space, iscalculated, and a hue angle h (h₁₅, hereinafter) at 15° is calculatedaccording to h=tan⁻¹(b*/a*), thereby finding a characteristic quantitysin (h₁₅) and a characteristic quantity cos (h₁₅). Similarly, based onthe spectral reflectance at 45°, a characteristic quantity c*₄₅ iscalculated, and a hue angle h₄₅ is calculated, thereby finding acharacteristic quantity sin (h₄₅) and a characteristic quantity cos(h₄₅). Further, based on the spectral reflectance at 110°, acharacteristic quantity c*₁₁₀ is calculated, and a hue angle h₁₁₀ iscalculated, thereby finding a characteristic quantity sin (h₁₁₀) and acharacteristic quantity cos (h₁₁₀).

Next, based on the spectral reflectance at 15°, Y₁₅, which is a Y valuein the XYZ color space, is calculated. Similarly, Y₄₅ is calculatedbased on the spectral reflectance at 45°, and a characteristic quantityFF(15,45) is calculated according to 2×(Y₁₅−Y₄₅) (Y₁₅+Y₄₅). These 19kinds of characteristic quantities thus obtained are associated with thepaint color codes, and are stored in the recording unit 13.

In Step S35, a neural network is constructed, and a process for trainingthe neural network is carried out using the characteristic quantities ofthe sample paint colors as training data. As in Step S15, a neuralnetwork having one intermediate layer consisting of 30 units isconstructed. In the neural network, each unit of the input layercorresponds to one of the characteristic quantities (19 kinds)expressing the color categories, which are defined in Step S34, whileeach unit of the output layer corresponds to one of the color categoryterms (10 kinds). Then, after setting a training rate to 0.5 and apermissible error to 0.2, the process for training the neural network iscarried out. The information for reconstructing the determined neuralnetwork, i.e., information regarding the characteristic quantities andcolor category terms corresponding to the units of the input layer andthe output layer, and the construction information of the neuralnetwork, such as synaptic weight data and the like of the neural networkdetermined through the training, are stored in the recording unit 13.

In Step S36, using the neural network after the training process, thecolor category evaluation value is found for each stock color, and theobtained color category evaluation values are associated with the paintcolor codes and stored in the recording unit 13, thereby completing acolor category database. More specifically, as in Step S16, a neuralnetwork is constructed using information for constructing the neuralnetwork determined in Step S35, the characteristic quantities (19 kinds)for each stock color found in Step S34 are inputted to each unit of theinput layer, and the values obtained from the units of the output layers(a real value ranging from 0 to 1) are associated with the paint colorcodes as corresponding color category evaluation values and stored inthe recording unit 13, thereby completing a color category database.

The following describes a method of searching for a paint color thatbelongs to the desired color category from the created color categorydatabase.

In Step S41, as in Step S21, a request for a desired color category isreceived as a search query. For example, when searching for a paintcolor belonging to a green-based color category, assuming that theparameter corresponding to the color category “Green” is “green”, thesearch query is “green=1”.

In Step S42, a search is carried out in the color category database tofind a paint color that matches the search query. As in Step S22, whenthe evaluation value of the color category is a number other than 0 or1, the determination as to whether the corresponding paint color belongsto the desired category is carried out as follows. For example, it isdetermined that the paint color expressed by the paint color code“belongs (1)” to the color category of the largest color categoryevaluation value among the plural color categories of the paint colorcode, and “does not belong (0)” to the remaining color categories.

More specifically, in Step S42, among the color category evaluationvalues (10 kinds) stored in the color category database, determinationis carried out as to whether the particular color category evaluationvalue (real value) specified by a search query that “belonging to acolor category (parameter=1)” is the maximum value among all of thecolor category evaluation values of the same paint color code, therebydetermining that the said paint color matches the search query or not.Among plural paint color codes of stock colors, the paint color code ofthe paint color that matches the search query is given as a searchresult. For example, when searching for a paint color belonging to agreen-based color category, because a search query “green=1”, a searchis carried out among the color category evaluation values stored in thecolor category database, so as to find a paint color code in which theevaluation value of the color category “Green” that corresponds to aparameter “green” is the maximum value among all of the color categoryevaluation values of the same paint color code. This paint color code ofthe paint color that matches the search query is given as a searchresult.

In Step S43, as in Step S23, the paint color code and paintcolor-related information as the search result are displayed in thedisplay apparatus.

Although the present invention is explained with a specific embodiment,the present invention is not limited to the specific embodimentsdescribed above.

Although the texture database and the color category database areseparately created in the above embodiment, it is possible to create aunified database consisting of the texture database and the colorcategory database, and carry out a search for a paint color that matchesthe desired texture and/or the color category with the unified database.More specifically, in accordance with the processes of Step S11 to StepS16 and Step S31 to Step S36 described in the first and secondembodiments, the search may be carried out such that a database iscreated by associating texture evaluation values and color categoryevaluation values with the paint color codes for each of the stockcolors, and in accordance with the processes of Step S21 to Step S23 andStep S41 to Step S43 described in the first and second embodiments, apaint color that matches the search query is found from the createddatabase according to a search query that specifies the desired textureand/or the color category. This method enables an easy search for apaint color, for example, “a green-based paint color having solidity butnot having a chromaticness appearance”, by using a search query thatspecifies both the desired texture and the color category requested bythe user at the time of paint color design. Alternatively, instead ofseparately creating one neural network for creating a texture databaseand the other neural network for creating a color category database, andcarrying out separate training processes for those neural networks, itis possible to carry out the training process for a single neuralnetwork instead of those networks.

Eliminating bias toward a specific texture is not always required to betaken into account in the selection of a sample paint color. Insofar asthe process selects at least one sample paint color having a texture ofone of the impression terms shown in Table 1, the rest of the samplepaint colors may be selected at random from the stock colors. Similarly,when creating the color category database, insofar as at least onesample paint color that appears to belong to a color category expressedby the color category term is selected, the rest of the sample paintcolors may be selected at random from the stock colors.

Although the evaluation of the textures and the color categories of thesample paint colors are carried out by experienced designers in theabove embodiment, the evaluation may be carried out by other evaluatorswho have a steady evaluation standard and are capable of evaluating thetextures and color categories of paint colors based on the specificevaluation standard. For example, the evaluation may be carried out bycolor designers of the industrial products, or engineers of the paintcompany.

Although the evaluation of the textures and the color categories arecarried out by a plurality of experienced designers in the aboveembodiment, the evaluation may be carried out by a single evaluator. Inthis case, it is preferable to eliminate the influence of the learningeffect by, for example, carrying out the evaluation of the textures andthe color categories a plurality of times.

Although, in the above embodiment, a texture evaluation value is set to“1” when the paint color appears to have the texture and to “0” when thepaint color does not appear to have the texture, i.e., the textureevaluation value is expressed as the presence/absence (two scales) ofthe texture, the texture evaluation value may be expressed by three orfive scales. Also in this case, the texture evaluation value forspecifying the presence/absence of the texture is set in the entry field51 as a search query, the relative magnitude between the textureevaluation value expressed by a plurality of scales set as the searchquery and the texture evaluation value stored in the texture database iscompared, thereby determining that the paint color matches the searchquery or not.

Although, in the above embodiment, characteristic quantities (the above26 kinds of characteristic quantities for the textures, and the above 19kinds of characteristic quantities for the color categories) found fromthe spectral reflectances are used as the characteristic quantities forexpressing the textures or the color categories of the paint colors, itis also possible to use the tristimulus values XYZ (CIE1964 color space)or the spectral reflectances in the visible light region as thecharacteristic quantities.

Although, in the above embodiment, the presence/absence of the texturesare determined by setting the threshold to a predetermined fixed value(0.5) and comparing the relative magnitude between the textureevaluation values stored in the database and the threshold value, it isalso possible to use the permissible error as the threshold, anddetermine that the paint color “has the texture (1)” when the textureevaluation value stored in the database is greater than the threshold.For example, if the evaluation value of the “deepness appearance” is 0.5and the permissible error (i.e., threshold) is 0.1, it is determinedthat the paint color has the texture “deepness appearance”. This enablesthe system to accurately find all of the paint colors having the targettexture.

Further, it is also possible to use (1.0-permissible error) as thethreshold, and determine that the paint color “has the texture (1)” whenthe texture evaluation value stored in the database is greater than thethreshold. For example, if the evaluation value of the “deepnessappearance” is 0.5 and the permissible error is 0.1, it is determinedthat the paint color does not have the texture “deepness appearance”.This enables the system to eliminate a noise (wrong search results)during the search for paint colors having the target texture.

Further, as shown in the following formula, it is possible to calculatethe difference (matching degree) between the evaluation value stored inthe database and the value included in the search query, and display thecalculated results in the sorted form in order of the size of matchingdegree.

matching degree of textures=Σ_(i)(1.0−y ₁(i))+Σ_(j)(y ₂(j))

In the formula, i and j are numbers given to the individual textureswith no overlaps. More specifically, y₁(i) is an evaluation value of thetexture (i) stored in the database and specified by the search query“the color has the texture”, and y₂(j) is an evaluation value of thetexture (j) stored in the database and specified by the search query“the color does not have the texture”. Σ_(i) and Σ_(j) are operators tofind the sums of i and j, respectively.

Although, in the above second embodiment, only one color category isdetermined for each sample paint color, the system can allot a pluralityof categories for one paint color. For example, a paint color hasmulticolor effect that varies in hue from, e.g., blue to green,depending on the observation angle, the paint color may be determined tobelong to both blue and green color categories. Similarly, a given paintcolor may be determined to belong to both beige and yellow colorcategories.

Although, in the above embodiment, the presence of the texture of thepaint color or the color category to which the paint color belongs isdetermined by storing the output value (a real value ranging from 0to 1) of the neural network in the database as it is, and by carryingout a search for the texture through comparing the relative magnitudebetween the texture evaluation values and the predetermined threshold,or by carrying out a search for the color category through determiningwhether the particular evaluation value is the maximum value among allof the evaluation values of the color categories of the same paint colorcode, the present invention is not limited to this method. For example,when storing the predicted evaluation value of the texture or thepredicted evaluation value of the color category, the presence of thetexture of the target color or the color category to which the targetcolor belongs may be determined and the determined results are stored inthe database as a value of 1 or 0. In this case, the step of determiningthe presence of the texture of the target color or the color category towhich the target color belongs during the search is not necessary. Thisincreases the search speed.

If the texture or the color category specified as a search query by theuser greatly differ from the texture or the color category of thecomputer graphic image displayed as the search result, the user maydetermine the evaluation value of the texture or the evaluation value ofthe color category of the displayed image, and, in a similar manner asthe above described process, a process of retraining the neural networkmay be carried out. This retraining provides more appropriate synapticweight data, thereby increasing the search accuracy.

Example 1

Hereunder, the present invention is described in further detail withreference to Examples. In Examples 1 and 2, two procedures wereconducted in advance as described below. First, spectral reflectances(light-receiving angles: 15, 25, 45, 75 and 110 degrees) andmicro-brilliance appearance data (HG, HB, HBL and SB) for all of thestock colors were associated with paint color codes, and were stored ina recording unit in advance. Secondly, a characteristic quantitycalculator program, which calculates 26 kinds of characteristicquantities to be used for creating a texture database and 19 kinds ofcharacteristic quantities to be used for creating a color categorydatabase; and a training program for a neural network were stored in therecording unit in advance. In Example 1, terms as shown in Table 1 wereused as impression terms.

(1-1) Selection of Paint Color Samples

First, according to a method taught in Patent Document 2 (JapaneseUnexamined Patent Publication No. H11-211569), representative angles Dwere determined for each of the stock colors based on the data ofspectral reflectances measured in the highlight side and in the shadeside. Next, a multi-angle Spectrophotometer MA68II, product of X-Rite,Inc., was used to measure, at the representative angles D, the spectralreflectances of coating films produced by applying each of the stockcolors. Then, based on the measured spectral reflectances, values of a*and b* in the L*a*b* color space were calculated, and the hue angle hwas thereby calculated according to h=tan⁻¹(b*/a*).

Next, based on the calculated hue angles h (degree), all of the stockcolors were classified into 4 groups; i.e., red group (0≦h<45,315≦h<360), yellow group (45≦h<135), green group (135≦h<225) and bluegroup (225≦h<315).

Thereafter, for the stock colors classified into 4 groups, a total of 4kinds of texture maps of the red, yellow, green and blue groups wereproduced. A texture map is a map which is produced by arranging aplurality of paint colors on a two-dimensional plane using coordinateaxes of two parameters that represent color and texture of the paintcolors so that textures of paint colors are easily classified. The twoparameters are a first principal component and a second principalcomponent, which are determined by: determining at least threecharacteristic quantities based on a plurality of spectral reflectancesthat are measured at a plurality of light-receiving angles for each ofthe paint colors; and subjecting a data group including the at leastthree characteristic quantities to principal component analysis. The twoparameters, the first principal component and the second principalcomponent, are specifically a parameter representing the shadingappearance of each paint color and a parameter representing theappearance heaviness of each paint color.

Next, each of the produced texture maps was partitioned at equalintervals along each direction of the vertical axis and horizontal axis.From each of the regions obtained as a result of the partition, onepaint color was selected as a paint color sample. For example, paintcolor samples may be selected by: displaying a texture map on a displayapparatus; and selecting, via an operating unit, a paint color shown ineach of the partitioned regions in the texture map. 200 paint colorsamples were selected in the manner described above.

(1-2) Texture Evaluation of Paint Color Samples

For all 200 paint colors selected as paint color samples, computergraphic images of the paint color samples were displayed on a displayapparatus, with which experienced designers with 10 or more years ofexperience designing paint colors evaluated the 10 kinds of texturesexpressed with the impression terms shown in Table 1, to thereby createthe texture evaluation sheet shown in Table 2. The thus-obtained textureevaluation values were inputted via an operating unit so as to be storedin a recording unit 13.

(1-3) Constructing and Retraining of Neural Network

As training data, characteristic quantities and texture evaluationvalues of 135 paint colors which were arbitrarily selected from theselected 200 paint color samples were utilized. First, based on thespectral reflectance data and micro-brilliance appearance data stored inthe recording unit in advance, an arithmetic unit calculated 26 kinds ofcharacteristic quantities, which represent textures, using thecharacteristic quantity calculation program stored in the recording unitin advance. Subsequently, a neural network was constructed; and thecalculated 26 kinds of characteristic quantities were associated witheach unit of an input layer, and the obtained 10 kinds of textureevaluation values were associated with each unit of an output layer. Thetraining rate was then set to 0.5 with a tolerance of 0.1, and theretraining of the neural network was carried out.

In order to verify the accuracy of the neural network after training,using this neural network after training, texture evaluation values for65 paint colors that had not been utilized for the training among theselected 200 paint color samples were evaluated. With respect thereto,the threshold was set to 0.5, and the output data in the neural networkwere binarized and then compared with the texture evaluation values ofthe corresponding paint colors stated in the evaluation sheet; thereby,verification was carried out to determine whether the textures of the 65paint colors that had not been utilized for the training were accuratelypredicted. Table 4 shows the results.

TABLE 4 Table 4 Three- Deepness dimensional Translucent Color SmoothClear Metallic Appearance Appearance Chromaticness Appearance NuanceSolid Band Appearance Feeling Appearance FAR (%) 0 27 20 0 16 18 10 30 00 FRR (%) 0 3 9 5 17 2 20 9 2 20 Accuracy Rate (%) 100 86 89 95 83 95 8288 98 83

The term “FAR” (False Acceptance Rate) used herein represents thepercentage of the paint colors that were incorrectly predicted as nothaving a texture regardless of them having the texture (a leak rate);the term “FRR” (False Rejection Rate) represents the percentage of thepaint colors that were incorrectly predicted as having a textureregardless of them not having the texture (a noise rate); and the term“Accuracy Rate” represents the percentage of the paint colors whosetextures were correctly predicted. As shown in Table 4, the neuralnetwork after training precisely predicted the textures of the paintcolors at a high accuracy of about 80% or more. Accordingly, the neuralnetwork was appropriately determined.

(1-4) Creation of Texture Database

Using the determined neural network, the texture evaluation values forabout 20,000 stock colors were determined. The determined values wereassociated with the paint color codes of each paint color so as to bestored; thereby, a texture database was created.

(1-5) Search for a Paint Color with Texture

The requests of a desired texture expressed with an impression term wereentered, and paint colors having the corresponding texture were searchedin the database. Specifically, paint colors “with deepness appearance”were searched, and computer graphic images of the 2,456 paint colorsretrieved were displayed on a display apparatus. Then, the experienceddesigner, who also evaluated the textures of the paint color samples,evaluated the textures of the displayed paint colors, and confirmed thatpaint colors “with deepness appearance” had been retrieved.

Additionally, two or more textures were combined to carry out a searchfor the corresponding paint colors. Specifically, paint colors that were“solid, not chromatic and not brightened” were searched, and computergraphic images of the 1,240 paint colors retrieved were displayed on thedisplay apparatus. Then, the experienced designer, who also evaluatedthe textures of the paint color samples, evaluated the textures of thedisplayed paint colors, and confirmed that paint colors that were“solid, not chromatic and not brightened” had been retrieved.

Example 2

In Example 2, terms that make a paint color easily imaged were used asimpression terms.

(2-1) Selection of Paint Color Samples

As paint color samples, 100 paint colors were arbitrarily selected fromthe 12,000 stock colors.

(2-2) Texture Evaluation of Paint Color Samples

For all 100 paint colors selected as paint color samples, computergraphic images of the paint color samples were displayed on a displayapparatus, with which experienced designers evaluated the 2 kinds oftextures expressed with the two impression terms “red as wine” and “blueas ice”, to thereby create the texture evaluation sheet. Among the 100paint color samples, 6 colors were evaluated as having a textureexpressed with the term “red as wine”, and 9 colors were evaluated ashaving a texture expressed with the term “blue as ice”. Thethus-obtained texture evaluation values were inputted via an operatingunit so as to be stored in the recording unit 13.

(2-3) Constructing and Retraining of Neural Network

A neural network was constructed, and the training thereof was carriedout in the same manner as in Example 1 (1-3). As training data,characteristic quantities and texture evaluation values of 65 paintcolors which were arbitrarily selected from the selected 100 paint colorsamples were utilized. The selected 65 paint color samples included the4 paint color samples that were evaluated as having a texture expressedwith the term “red as wine”, and 5 samples that were evaluated as havinga texture expressed with the term “blue as ice”.

The neural network after training was used to determine textureevaluation values for 35 paint colors that had not been utilized for thetraining among the selected 100 paint color samples, thereby theaccuracy of the neural network after training was verified. Theverification was conducted in the same manner as in Example 1 (1-3).Referring to the determined texture evaluation values, the textures ofthe 35 paint color samples that had not been utilized for the trainingwere precisely predicted. Accordingly, the neural network wasappropriately determined.

(2-4) Creation of Texture Database

A texture database was created in the same manner as in Example 1 (1-3).

(2-5) Search for a Paint Color with Texture

The requests of a desired texture expressed with an impression term wereentered, and paint colors having the corresponding texture were searchedin the database. Specifically, paint colors with a “red as wine”appearance, and paint colors with a “blue as ice” appearance wererespectively searched.

First, paint colors with a “red as wine” appearance were searched, andcomputer graphic images of the 66 paint colors retrieved were displayedon a display apparatus. Then, the experienced designer, who alsoevaluated the textures of the paint color samples, evaluated thetextures of the displayed paint colors. As a result, 54 paint colors outof the 66 colors retrieved were those with a “red as wine” appearance(accuracy rate: 81.8%). Subsequently, paint colors with a “blue as ice”appearance were searched and, similarly as for the “red as wine”texture, the experienced designer evaluated the computer graphic imagesof the 87 paint colors retrieved. As a result, 73 paint colors out ofthe 87 colors retrieved were those with a “blue as ice” appearance(accuracy rate: 83.9%). Accordingly, paint colors with a desired texturewere retrieved from the database at a high accuracy of about 80% ormore.

INDUSTRIAL APPLICABILITY

The method of creating a database for searching for a paint color, thesearch method using the database, and the system, the program, and therecording medium therefor according to the present invention enables thecreation of a database in which the textures or the color categories ofthe paint colors are associated with codes each of which is specific toeach paint color. Further, using the database, a set of stock colors canbe narrowed down with high accuracy to a paint color that matches adesired texture, or a paint color that belongs to a desired colorcategory.

1. A method for creating a paint color database, comprising: a firststep of storing spectral reflectance data of a plurality of paint colorsin a recording unit after associating each spectral reflectance datawith a code for specifying each paint color; a second step of storingcolor category evaluation values of sample paint colors selected fromthe plurality of paint colors in the recording unit after associatingthe each color category evaluation value with the code; a third step ofcalculating characteristic quantities of the paint colors expressingcolor categories using the spectral reflectance data, and storing thecharacteristic quantities in the recording unit after associating theeach characteristic quantity with the code; a fourth step of carryingout a process for training a neural network having input unitscorresponding to the characteristic quantities and output unitscorresponding to the color category evaluation values, using thecharacteristic quantities and the color category evaluation values ofthe sample paint colors as training data; and a fifth step of inputtingcharacteristic quantities of the paint colors other than the samplepaint colors into the neural network after the training process, andstoring output data in the recording unit after associating the eachoutput data with the code.
 2. The method for creating a paint colordatabase according to claim 1, wherein: the characteristic quantitiesinclude: a characteristic quantity L*₁₅, a characteristic quantity a*₁₅,and a characteristic quantity b*₁₅, which are L* value, a* value, and b*value calculated from a spectral reflectance at an observation angle of15° in the L*a*b* color space, a characteristic quantity L^(*) ₄₅, acharacteristic quantity a*₄₅, and a characteristic quantity b*₄₅, whichare L* value, a* value, and b* value calculated from a spectralreflectance at an observation angle of 45° in the L*a*b* color space, acharacteristic quantity L*₁₁₀, a characteristic quantity a*₁₁₀ and acharacteristic quantity b*₁₁₀, which are L* value, a* value, and b*value calculated from a spectral reflectance at an observation angle of110° in the L*a*b* color space; a characteristic quantity c*₁₅ that is ac* value calculated from a spectral reflectance at an observation angleof 15° in the L*C*h* color space, a characteristic quantity c*₄₅ that isa c* value calculated from a spectral reflectance at an observationangle of 45° in the L*C*h* color space, and a characteristic quantityc*₁₁₀ that is a c* value calculated from a spectral reflectance at anobservation angle of 110° in the L*C*h* color space; a characteristicquantity sin (h₁₅); a characteristic quantity cos(h₁₅), a characteristicquantity sin(h₄₅), a characteristic quantity cos(h₄₅), a characteristicquantity sin(h₁₁₀)) and a characteristic quantity cos(h₁₁₀) wherein h₁₅represents a hue angle at an observation angle of 15°, h₄₅ represents ahue angle at an observation angle of 45°, h₁₁₀ represents a hue angle atan observation angle of 110°, which are found from the a*₁₅, b*₁₅, a*₄₅,b*₄₅, a*₁₁₀, and b*₁₁₀ according to h=tan⁻¹(b*/a*); and a characteristicquantity FF(15,45), which is found according to 2×(Y₁₅−Y₄₅)/(Y₁₅+Y₄₅),wherein Y₁₅ represents a Y value calculated from a spectral reflectanceat an observation angle of 15° in the XYZ color space, and Y₄₅represents a Y value calculated from a spectral reflectance at anobservation angle of 45° in the XYZ color space.
 3. A method forsearching for a paint color in a paint color database created by themethod for creating a paint color database according to claim 1,comprising: a first step of receiving, as a search query, a colorcategory and an evaluation value that denotes a degree of attribution tothe color category; a second step of retrieving an evaluation valuecorresponding to the color category from the database, and determiningwhether the evaluation value is a value indicating the attribution tothe color category; and a third step of, if determining that theevaluation value is a value indicating the attribution to the colorcategory in the second step, giving a corresponding paint color code asa search result.
 4. A system for creating a paint color database,comprising: an arithmetic unit having a recording unit; and aspectrophotometer, wherein: the arithmetic unit measures spectralreflectance data for each of a plurality of paint colors using thespectrophotometer, the arithmetic unit stores the spectral reflectancedata of the paint colors in the recording unit after associating eachspectral reflectance data with a code for specifying each paint color,the arithmetic unit stores color category evaluation values of samplepaint colors selected from the paint colors in the recording unit afterassociating the each color category evaluation value with the code, thearithmetic unit calculates characteristic quantities of the paint colorsexpressing color categories using the spectral reflectance data, andstores the characteristic quantities in the recording unit afterassociating the each characteristic quantity with the code, thearithmetic unit carries out a process for training a neural networkhaving input units corresponding to the characteristic quantities andoutput units corresponding to the color category evaluation values,using the characteristic quantities and the color category evaluationvalues of the sample paint colors as training data; and the arithmeticunit inputs characteristic quantities of the paint colors other than thesample paint colors into the neural network after the training process,and stores output data in the recording unit after associating the eachoutput data with the code.
 5. A system for searching for a paint colorin a paint color database created by the method for creating a paintcolor database according to claim 1, comprising: an arithmetic unithaving a recording unit storing the database, wherein: the arithmeticunit receives, as a search query, a color category and an evaluationvalue that denotes a degree of attribution to the color category, thearithmetic unit retrieves an evaluation value corresponding to the colorcategory from the database, and determines whether the evaluation valueis a value indicating the attribution to the color category; and ifdetermining that the evaluation value is a value indicating theattribution to the color category in the second step, the arithmeticunit gives a corresponding paint color code as a search result.
 6. Aprogram for creating a paint color database by causing a computer torealize: a first function for storing spectral reflectance data of aplurality of paint colors in a recording unit after associating eachspectral reflectance data with a code for specifying each paint color; asecond function for storing color category evaluation values of samplepaint colors selected from the plurality of paint colors in therecording unit after associating the each color category evaluationvalue with the code; a third function for calculating characteristicquantities of the paint colors expressing color categories using thespectral reflectance data, and storing the characteristic quantities inthe recording unit after associating the each characteristic quantitywith the code; a fourth function for carrying out a process for traininga neural network having input units corresponding to the characteristicquantities and output units corresponding to the color categoryevaluation values, using the characteristic quantities and the colorcategory evaluation values of the sample paint colors as training data;and a fifth function for inputting characteristic quantities of thepaint colors other than the sample paint colors into the neural networkafter the training process, and storing output data in the recordingunit after associating the each output data with the code.
 7. A programfor searching for a paint color in a paint color database created by themethod for creating a paint color database according to claim 1, bycausing a computer to realize: a first function of receiving, as asearch query, a color category and an evaluation value that denotes adegree of attribution to the color category; a second function ofretrieving an evaluation value corresponding to the color category fromthe database, and determining whether the evaluation value is a valueindicating the attribution to the color category; and a third functionof, if determining that the evaluation value is a value indicating theattribution to the color category in the second step, giving acorresponding paint color code as a search result.
 8. Acomputer-readable recording medium storing the program for creating apaint color database according to claim
 6. 9. A computer-readablerecording medium storing the program for searching for a paint coloraccording to claim 7.